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WIREs Cogn Sci
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Inductive reasoning 2.0

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Inductive reasoning entails using existing knowledge to make predictions about novel cases. The first part of this review summarizes key inductive phenomena and critically evaluates theories of induction. We highlight recent theoretical advances, with a special emphasis on the structured statistical approach, the importance of sampling assumptions in Bayesian models, and connectionist modeling. A number of new research directions in this field are identified including comparisons of inductive and deductive reasoning, the identification of common core processes in induction and memory tasks and induction involving category uncertainty. The implications of induction research for areas as diverse as complex decision‐making and fear generalization are discussed. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Learning
Examples of tree, dimensional, and causal‐structured representations. Adapted from Kemp and Tenenbaum (). Solid arrows show predator–prey relations. Light arrows show shared habitat relations
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Example of the experimental design used by Dunsmoor and Murphy () (CS = Conditioned stimulus; GS = Generalization stimulus)
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Two‐dimensional model of reasoning, showing arguments varying in apparent deductive correctness (y‐axis) and consistency with associative knowledge (x‐axis). The dotted line shows the decision boundary for judging whether an argument is deductively valid or invalid. The dashed line shows the decision boundary for judging whether an argument is inductively strong or weak. Adapted from Heit and Rotello ()
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